Institute of Medical Sciences, School of Medicine, University of Aberdeen, Aberdeen, UK.
Department of Radiology, Royal Marsden Hospital, London, UK.
Sci Rep. 2020 Nov 18;10(1):20041. doi: 10.1038/s41598-020-74397-y.
Lipid composition in breast cancer, a central marker of disease progression, can be non-invasively quantified using 2D MRS method of double quantum filtered correlation spectroscopy (DQF-COSY). The low signal to noise ratio (SNR), arising from signal retention of only 25% and depleted lipids within tumour, demands improvement approaches beyond signal averaging for clinically viable applications. We therefore adapted and examined combination algorithms, designed for 1D MRS, for 2D MRS with both internal and external references. Lipid composition spectra were acquired from 17 breast tumour specimens, 15 healthy female volunteers and 25 patients with breast cancer on a clinical 3 T MRI scanner. Whitened singular value decomposition (WSVD) with internal reference yielded maximal SNR with an improvement of 53.3% (40.3-106.9%) in specimens, 84.4 ± 40.6% in volunteers, 96.9 ± 54.2% in peritumoural adipose tissue and 52.4% (25.1-108.0%) in tumours in vivo. Non-uniformity, as variance of improvement across peaks, was low at 21.1% (13.7-28.1%) in specimens, 5.5% (4.2-7.2%) in volunteers, 6.1% (5.0-9.0%) in peritumoural tissue, and 20.7% (17.4-31.7%) in tumours in vivo. The bias (slope) in improvement ranged from - 1.08 to 0.21%/ppm along the diagonal directions. WSVD is therefore the optimal algorithm for lipid composition spectra with highest SNR uniformly across peaks, reducing acquisition time by up to 70% in patients, enabling clinical applications.
乳腺癌的脂质组成是疾病进展的一个重要标志物，可以使用二维磁共振波谱（2D MRS）方法的双量子过滤相关光谱学（DQF-COSY）对其进行非侵入性定量。由于只有 25%的信号保留和肿瘤内脂质被耗尽，导致信噪比（SNR）较低，因此需要采用信号平均以外的改进方法，以实现临床可行的应用。因此，我们对适用于 1D MRS 的组合算法进行了调整和检验，以用于具有内部和外部参考的 2D MRS。我们在临床 3T MRI 扫描仪上从 17 个乳腺癌标本、15 名健康女性志愿者和 25 名乳腺癌患者中采集了脂质组成谱。采用内部参考的白化奇异值分解（WSVD）方法可获得最大 SNR，在标本中可提高 53.3%（40.3-106.9%），在志愿者中提高 84.4±40.6%，在肿瘤周围脂肪组织中提高 96.9±54.2%，在体内肿瘤中提高 52.4%（25.1-108.0%）。改进的不均匀性（即跨峰改进的方差）在标本中为 21.1%（13.7-28.1%），在志愿者中为 5.5%（4.2-7.2%），在肿瘤周围组织中为 6.1%（5.0-9.0%），在体内肿瘤中为 20.7%（17.4-31.7%）。沿对角线方向，改进的偏差（斜率）范围从-1.08 到 0.21%/ppm。因此，WSVD 是具有最高 SNR 的脂质组成谱的最佳算法，可在跨峰均匀提高 SNR，在患者中最多可将采集时间减少 70%，从而实现临床应用。
